24 citations · 29 across the 6 of their papers we have counts for
5 papers · 1 filter
Testing the Feasibility of Linear Programs with Bandit Feedback
Aditya Gangrade, Aditya Gopalan, Venkatesh Saligrama +1
While the recent literature has seen a surge in the study of constrained bandit problems, all existing methods for these begin by assuming the feasibility of the underlying problem…
A Unified Framework for Discovering Discrete Symmetries
Pavan Karjol, Rohan Kashyap, Aditya Gopalan +1
We consider the problem of learning a function respecting a symmetry from among a class of symmetries. We develop a unified framework that enables symmetry discovery across a broad…
On the Minimax Regret for Linear Bandits in a wide variety of Action Spaces
Debangshu Banerjee, Aditya Gopalan
As noted in the works of \cite{lattimore2020bandit}, it has been mentioned that it is an open problem to characterize the minimax regret of linear bandits in a wide variety of acti…
Actor-Critic based Improper Reinforcement Learning
Mohammadi Zaki, Avinash Mohan, Aditya Gopalan +1
We consider an improper reinforcement learning setting where a learner is given base controllers for an unknown Markov decision process, and wishes to combine them optimally to…
Low-rank Bandits with Latent Mixtures
Aditya Gopalan, Odalric-Ambrym Maillard, Mohammadi Zaki
We study the task of maximizing rewards from recommending items (actions) to users sequentially interacting with a recommender system. Users are modeled as latent mixtures of C man…